Reducto

ETL for LLMs — turn PDFs, decks, and emails into clean, structured data.

Ingests 64+ file types and partitions, chunks, enriches, and embeds them into LLM-ready output, handling OCR, tables, and document hierarchy. An open-source library plus a low-code platform and API; a staple preprocessing layer for production RAG.

Toolkit that turns documents into AI-ready Markdown and JSON.

A document-processing toolkit that converts PDF, DOCX, PPTX, XLSX, HTML, images, and audio into clean Markdown or JSON for LLM and RAG pipelines. It does advanced PDF understanding — page layout, reading order, table structure, and OCR for scans — and ships a hybrid chunker plus native LangChain and LlamaIndex integrations. Small enough to run on a laptop via a Python API or CLI; MIT-licensed and community-governed.

Open-source document intelligence API for RAG-ready data.

A document parsing and intelligence API that turns complex PDFs, slides, Word docs, and images into clean, LLM/RAG-ready chunks. Chunkr runs layout analysis, OCR, reading-order detection, semantic chunking, and schema-based extraction, emitting HTML, Markdown, or JSON. Self-host the open-source pipeline or call the managed cloud API, which includes a free tier of 200 pages with no card required.

AI agents for document processing and enterprise data extraction.

Nanonets automates document-heavy workflows — invoices, orders, contracts, and claims — with AI agents that read, extract, and route structured data across ERPs, email, and approval chains. It runs on its own OCR-3 extraction model and can fold in LLMs for agentic pipelines. Offered as managed cloud with VPC, single-tenant, and on-premises deployment options and regional data residency.